Amazon's New Simple Workflow Service

Yesterday, Amazon launched an adjunct to its successful Amazon Web Service (AWS) elastic cloud offering. While we don’t normally comment on every product release, this one is significant — primarily because of who is doing it. The Simple Workflow service (SWF) clearly has nothing to do with Adobe’s Flash offering (although techno-nerds may initially think so, given the acronym).

So what was this all about? The business model is certainly interesting: an elastic, configurable workflow capability that’s distributed across any number of access points. Essentially, this will allow an organization to orchestrate processes in the cloud, linking participants up and down the value chain.

“Amazon Simple Workflow Service (Amazon SWF) is a workflow service for building scalable, resilient applications. Whether automating business processes for finance or insurance applications, building sophisticated data analytics applications, or managing cloud infrastructure services, Amazon SWF reliably coordinates all of the processing steps within an application.”

Pricing is initially free and then transitions into a blended, low-cost consumption model, with charges oriented around execution steps, bandwidth usage, how long the task is active, and signals/markers, etc. With usage charges at around $0.0001 per execution step, this gives you an idea of how small the operating overhead might be.

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Poor Data Quality: An Often Overlooked Cause Of Poor Customer Satisfaction Scores

Customer service managers don’t often realize that data quality projects move the needle on customer satisfaction. In a recent Forrester survey of members of the Association of Business Process Management Professionals (ABPMP), of the 45% who reported that they are working on improving CRM processes, only 38% have evaluated the impact that poor-quality data has on the effectiveness of these processes. And of the 37% of respondents working on customer experience for external-facing processes, only 30% proactively monitor data quality impacts. That’s no good; lack of attention to data quality leads to a set of problems:

  • Garbage in/garbage out erodes customer satisfaction. Agents need the right data about their customers, purchases, and prior service history at the right point in the service cycle to deliver the right answers. But when their tool sets pull data from low-quality data sources, agents don’t have the right information to answer their customers. An international bank, for example, could not meet its customer satisfaction goals because agents in its 23 contact centers all followed different operational processes, using up to 18 different apps — many of which contained duplicate data — to serve a single customer.
  • Lack of trust in data negatively affects agent productivity. Agents start to question the validity of the underlying data when data inconsistencies are left unchecked. This means that agents often ask a customer to validate product, service, and customer data during an interaction — increasing handle times and eroding trust.
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